【妹妹花名】:#甜甜 【报告署名】:加麻加辣轩 【验证时间】:8.28凌晨 【验证地点】:江宁大学城附近 【服务类别】:98 【修车水费】:600p(工兵优惠-300) 【妹妹类型】:精神小妹 【颜值身材】: {年龄}:目测19 {颜值}:人照8成(去掉美颜差不多,无照骗行为) {皮肤}:黄皮(纹身不少) {罩杯}:小a {身高}:160 {体重}:90左右 {赘肉}:无明显赘肉 {颜值综合打分}:7.5 {身材综合打分}:7.5 【服务质量】: {服务内容}:基本跟课表一致,确实嫩妹服务不多 {服务水平}:嫩妹刚下海 {服务态度}:服务态度挺好的,看的出来刚下海,没什么技巧经验,但是态度不错 【进阶服务】:有的打✅ 1️⃣舌吻 2️⃣69 3️⃣情绪价值 √ 4️⃣陪浴 5️⃣莞式(全套)服务 7️⃣毒龙 {服务综合打分}:5 【纹身疤痕】:有纹身,无疤痕 【是否吸烟】:老师吸烟(还给我递了一根,人挺好) 【课室卫生】…

Channel
泡芙女士模拟驾校(工兵报告厅)
@CJDABG
On this record: Growth · Engagement · Reactions · Posts · Posts edited after publishing · Citations · Handles named that no longer answer · Cite this entry
10,170subscribers
+548 since we began measuring on 8 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
Register entry
| Telegram ID | -1002194583134 |
|---|---|
| Type | Channel |
| Username | @CJDABG |
| Created | Between 1 June 2024 and 30 September 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 3 September 2026 |
| Measurements held | 16 |
| Confirmed unchanged | 1 time, most recently 3 September 2026 |
| On Telegram | t.me/CJDABG |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 3 Sept 2026, 22:16 | 10,170 | +6 |
| 2 Sept 2026, 15:45 | 10,164 | +15 |
| 1 Sept 2026, 16:39 | 10,149 | +26 |
| 31 Aug 2026, 13:45 | 10,123 | +16 |
| 30 Aug 2026, 10:47 | 10,107 | +18 |
| 29 Aug 2026, 07:13 | 10,089 | +10 |
| 28 Aug 2026, 05:12 | 10,079 | -3 |
| 27 Aug 2026, 07:53 | 10,082 | +13 |
| 26 Aug 2026, 04:55 | 10,069 | +1 |
| 25 Aug 2026, 03:53 | 10,068 | +74 |
| 21 Aug 2026, 19:15 | 9,994 | +85 |
| 18 Aug 2026, 08:32 | 9,909 | +81 |
| 15 Aug 2026, 01:24 | 9,828 | +117 |
| 11 Aug 2026, 07:12 | 9,711 | +82 |
| 8 Aug 2026, 08:37 | 9,629 | +7 |
| 8 Aug 2026, 05:32 | 9,622 | first reading |
Engagement
134 posts held, back to 3 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 35 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 5.93%
- avg views ÷ 10,170 subscribers
- Avg views / post
- 603
- 127 posts measured
- Reaction rate
- 0.16%
- reactions ÷ views · ER floor
- Posts in window
- 127
- of 134 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 43 of 127 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 28 August 2026 |
|---|---|
| Posts held | 134 (3 August 2026 – 28 August 2026) |
| Views total | 76,621 |
| Reactions total | 56 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 29 Aug 2026, 03:54 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Reaction mix
58 reactions across 38 posts, in 4 distinct kinds. The most used accounts for 94.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 55 | 94.8% | |
| 👍 | 1 | 1.72% | |
| 😁 | 1 | 1.72% | |
| 🤔 | 1 | 1.72% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 46 of the 134 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 58 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 134 most recent posts we hold, published 3 August 2026 to 28 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Recent posts
【南京[比奇堡]学生会工兵报告】 甜甜
4
3
【南京[比奇堡]学生会工兵报告】 2
【南京[比奇堡]学生会工兵报告】 【南京[比奇堡]学生会工兵报告】 😎 南京比奇堡 报告模板 【妹妹花名】:小鱼儿 【报告署名】:闲扯 里个儿梗 【验证时间】:8.28 【验证地点】:建邺区 龙湖天街 【服务类别】:98(大项) 【修车水费】:700p(工兵-300) 【妹妹类型] 御姐 【颜值身材】:颜值小巧,身材BBW {年龄}:21左右 {颜值}:比照片年轻 {皮肤}:浅小麦色 {罩杯}:D {身高}:170 {体重}:100斤左右吧 {赘肉}:肚肚有一点 {颜值综合打分}:8.8分 {身材综合打分}:8.8分 【服务质量】:口活好 {服务内容}:舌吻,69,陪浴,做 {服务水平}:口活不错 {服务态度}:愿意配合 【进阶服务】:有的打✅ 1️⃣舌吻 ✅ 2️⃣69✅ 3️⃣情绪价值 ✅ 4️⃣陪浴✅ 5️⃣莞式(全套)服务 7️⃣毒龙 {服务综合打分}:7.5分 【纹身疤痕】:无 【是否吸烟】:有,没闻到 【…
【妹妹花名】:#小鱼儿 【报告署名】:村长 【验证时间】:2026.8.27 【验证地点】:建邺区 【服务类别】:98 【修车水费】:700p工兵400p 【妹妹类型】:可爱肥臀妹妹 【颜值身材】:9 {年龄}:目测19 {颜值}:与照片差不多 {皮肤}:白皮肤,无可疑皮肤病,无纹身 {罩杯}:D胸型饱满 {身高}:165 {体重}:微胖,130 {赘肉}:肚子有,微胖正常 {颜值综合打分}:9 {身材综合打分}:9.5本人喜欢这种微胖身材 【服务质量】8 {服务内容}三件套,有陪浴,舌吻 {服务水平}:嫩妹 {服务态度}:服务态度没得挑,没有不舒服的地方 【进阶服务】: 1️⃣舌吻 √ 2️⃣69√ 3️⃣情绪价值 √ 4️⃣陪浴√ 5️⃣莞式(全套)服务 7️⃣毒龙 {服务综合打分}:9 【纹身疤痕】:无纹身和明显疤痕 【是否吸烟】:吸烟,但是会问你可以吸烟不 【课室卫生】:一次性用品齐全,室内、床褥整洁 【周边环境…
【南京[比奇堡]学生会工兵报告】 小鱼儿
3
2
【南京[比奇堡]学生会工兵报告】 【南京[比奇堡]学生会工兵报告】 【妹妹花名】:# 小禾 【报告署名】:gu 【验证时间】:8.27 【验证地点】:鼓楼复地 【服务类别】:98(大项) 【修车水费】:800p(工兵-4) 【妹妹类型】:御姐型 【颜值身材】 {年龄}:28左右 {颜值}:频道八分像 {皮肤}: 无可疑皮肤病,黄皮,无纹身 {罩杯}:a+ {身高}:166 {体重}:90 {赘肉}:没有 {颜值综合打分}:8.5 {身材综合打分}:8.5 【服务质量】: {服务内容}:亲咪咪,口,做 {服务水平}:嫩妹三件套 {服务态度}:态度热情 【进阶服务】:有的 1️⃣舌吻 2️⃣69 3️⃣情绪价值 4️⃣陪浴✅ 5️⃣莞式(全套)服务 7️⃣毒龙 {服务综合打分}:7 【纹身疤痕】:无纹身,无明显疤痕 【是否吸烟】:教室无烟味 【课室卫生】:环境干净,有一次性浴巾 【周边环境】:公寓,无门禁 【综合打分】:9 【…
【南京[比奇堡]学生会工兵报告】 【妹妹花名】:#小禾 【报告署名】:匿名 【验证时间】:8月底 【验证地点】:鼓楼区公寓(建议换个地方) 【服务类别】:98(大项) 【修车水费】:800p(工兵400) 【妹妹类型】:御姐 【颜值身材】: {年龄}:26 {颜值}:与照片视频相似度8成 {皮肤}:黄皮 {罩杯}:目测A {身高}:目测166 {体重}:目测100左右 {赘肉}:无赘肉 {颜值综合打分}:8分 {身材综合打分}:7分 【服务质量】: {服务内容}:陪浴 口 做 {服务水平}:一般 {服务态度}:服务细致耐心,无敷衍机车行为 【进阶服务】:有的打✅ 1️⃣舌吻 2️⃣69 3️⃣情绪价值 4️⃣陪浴✅ 5️⃣莞式(全套)服务 7️⃣毒龙 {服务综合打分}:6分 【纹身疤痕】:无纹身和疤痕 【是否吸烟】:无烟味 【课室卫生】:整洁干净 【周边环境】:鼓楼的公寓,不用刷卡停车方便 【综合打分】:7.5分 【综合…
Showing the 12 most recent of 134 posts we hold for @CJDABG. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Posts edited after publishing
@CJDABG edited 8 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
- First edit seen
- 10 August 2026
- Most recent edit
- 23 August 2026
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 4 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Handles this channel named that no longer answer
- Dead references
- 1
- handles named in this channel’s posts, vacant today
- Evidenced gone
- 0
- we ourselves saw one of these resolve, at some point
- Never seen alive
- 1
- vacant every time we have ever looked
@CJDABG named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
named in 3 posts, 9 August 2026 – 12 August 2026
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 3 September 2026 — this entry's latest reading, not the date you are reading this.
“泡芙女士模拟驾校(工兵报告厅)” (@CJDABG), 10,170 subscribers as measured 3 September 2026. Telegram Register, tgregister.com/channel/CJDABG.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.